Comparative Analysis of Customer Loan Approval Prediction using Machine Learning Algorithms

Tumuluru, Praveen; Burra, Lakshmi Ramani; Loukya, M.; Bhavana, S.; CSaiBaba, H.M.H.; Sunanda, N.

doi:10.1109/icais53314.2022.9742800

Cited by 19 publications

(3 citation statements)

References 9 publications

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“…Tumuluru et al ( 2022) [31] also used data preprocessing techniques, such as feature scaling, normalization, and one-hot encoding, as well as four machine learning algorithms, namely, logistic regression, random forest, k-nearest neighbor, and support vector machine, to predict loan approvals. They found that random forest had the highest accuracy of 81%, followed by logistic regression (77%), SVM (73.2%), and KNN (68%).…”

Section: Discussionmentioning

confidence: 99%

Machine Learning for an Enhanced Credit Risk Analysis: A Comparative Study of Loan Approval Prediction Models Integrating Mental Health Data

Alagic,

Zivic,

Kadusic

et al. 2024

MAKE

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The number of loan requests is rapidly growing worldwide representing a multi-billion-dollar business in the credit approval industry. Large data volumes extracted from the banking transactions that represent customers’ behavior are available, but processing loan applications is a complex and time-consuming task for banking institutions. In 2022, over 20 million Americans had open loans, totaling USD 178 billion in debt, although over 20% of loan applications were rejected. Numerous statistical methods have been deployed to estimate loan risks opening the field to estimate whether machine learning techniques can better predict the potential risks. To study the machine learning paradigm in this sector, the mental health dataset and loan approval dataset presenting survey results from 1991 individuals are used as inputs to experiment with the credit risk prediction ability of the chosen machine learning algorithms. Giving a comprehensive comparative analysis, this paper shows how the chosen machine learning algorithms can distinguish between normal and risky loan customers who might never pay their debts back. The results from the tested algorithms show that XGBoost achieves the highest accuracy of 84% in the first dataset, surpassing gradient boost (83%) and KNN (83%). In the second dataset, random forest achieved the highest accuracy of 85%, followed by decision tree and KNN with 83%. Alongside accuracy, the precision, recall, and overall performance of the algorithms were tested and a confusion matrix analysis was performed producing numerical results that emphasized the superior performance of XGBoost and random forest in the classification tasks in the first dataset, and XGBoost and decision tree in the second dataset. Researchers and practitioners can rely on these findings to form their model selection process and enhance the accuracy and precision of their classification models.

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Section: Discussionmentioning

confidence: 99%

Machine Learning for an Enhanced Credit Risk Analysis: A Comparative Study of Loan Approval Prediction Models Integrating Mental Health Data

Alagic,

Zivic,

Kadusic

et al. 2024

MAKE

View full text Add to dashboard Cite

show abstract

“…Tumuluru et al [37] noted that in today's increasingly competitive market, estimating the risk involved in a loan application is one of the most important challenges to the survival and profitability of banks. The study mentioned that most banks use credit scoring and risk assessment procedures to review loan applications and make loan approval decisions, yet every year many people fail to repay their loans or default on their loans.…”

Section: Related Research (İlgi̇li̇ Araştirmalar)mentioning

confidence: 99%

A Comparative Study of Loan Approval Prediction Using Machine Learning Methods

Sinap

2024

Gazi Üniversitesi Fen Bilimleri Dergisi Part C: Tasarım Ve Teknoloji

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Loan prediction plays an important role in the process of evaluating loan applications by financial institutions. Machine learning models can automate this process and make the lending process faster and more efficient. In this context, the main objective of this research is to develop models for loan approval prediction using machine learning algorithms such as Logistic Regression, K-Nearest Neighbors, Support Vector Machine, Decision Tree, and Random Forest and to compare their performances. In addition, determining the effect of K-Best and Recursive Feature Elimination feature selection methods on model performances is another important objective of the research. Furthermore, the evaluation of the effectiveness of techniques such as cross-validation (K-Fold) and Train, Test and Validation in measuring the performance of models is also among the objectives of the research. The findings revealed that married individuals are more likely to be approved for loans than single individuals, high income individuals more likely than low-income individuals, males more likely than females, and university graduates more likely than non-university graduates. According to the performance measures, Random Forest was the most successful algorithm with an accuracy rate of 97.71% in loan approval prediction. To achieve this accuracy rate, feature selection was performed with the Recursive Feature Elimination method and the measurement was made with the cross-validation method. It was found that the feature selection methods have a significant impact on the model performances and the Recursive Feature Elimination method was the most successful method. Moreover, the highest accuracy rate achieved by the Random Forest algorithm, which showed the highest performance in all cases, was measured by cross-validation.

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“…Other highly praised models such as the flexible support vector machine is also selected to fit the dataset [6]. Leaving out the final candidate models of linear models which stress the logical and marginal effect of each individual predictors: logistic regression, support vector machine (SVM); nonlinear models which aims to capture a more complex relationship between these variables: decision tree, Random Forest, Ada Boost and deep learning to provide insights and prediction in a most complex situation between elements: Neural Network [7][8][9][10][11][12].…”

Section: Introductionmentioning

confidence: 99%

Research on loan approval and credit risk based on the comparison of Machine learning models

Yang

2024

SHS Web Conf.

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Nowadays, home loan is a frequently accessed component of people’s financing activities. Homeowners wants to increase the probability of loan acceptance, however banks seek to borrow money to low risk customers. This paper compared and examined the machine learning models to select when loan applicants evaluating their probability of success. This paper introduced the recommended models for the problem, explanations on how to use the selected model. 6 candidate models, including Logistic regression, Decision tree, Random Forest, support vector machine (SVM), Ada Boost and Neural Network are selected. The model selection process would focus on the model’s accuracy on test data as well as the interpretability of these models. The models’ result was interpreted to derive optimal strategies to be undertaken by both debtors and creditors. Throughout comparison between these models, logistic regression was the best in terms of interpretability and accuracy. Nonetheless, other models could bolster the decision-making process by examining their confusion matrices and the fitted importance of predictors in each model. This paper revealed practical implications of machine learning theories on home loan approval and credit risk and aimed to help decision making for both debtors and creditors.

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Comparative Analysis of Customer Loan Approval Prediction using Machine Learning Algorithms

Cited by 19 publications

References 9 publications

Machine Learning for an Enhanced Credit Risk Analysis: A Comparative Study of Loan Approval Prediction Models Integrating Mental Health Data

Machine Learning for an Enhanced Credit Risk Analysis: A Comparative Study of Loan Approval Prediction Models Integrating Mental Health Data

A Comparative Study of Loan Approval Prediction Using Machine Learning Methods

Research on loan approval and credit risk based on the comparison of Machine learning models

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